From a purely business perspective, the concept of wellness is constricted to behavioural and technical elements. However, of vital importance to a human-centric approach to financial wellness, is considering the value it can create for the customer.
Humans often remain blissfully ignorant of their relationship with money, not consciously aware of nor tracking their behaviors or habits. Financial institutions have the opportunity to provide guidance in mitigating this, fostering a proactive and educated spending demeanour on the part of customers.
There are a few frameworks currently in place that fintechs use to help improve the human and social element, enabling customers to complete activities that will lead to financial wellness. This includes setting up a budget, creating budgeting goals, saving, or paying off debts. With algorithms backed by Computational Social Science, businesses can use scoring mechanisms to inform personalized plans and recommended actions for customers. For example, a lack of savings would produce negative scores, while a retirement plan produces positive scores. Based on these scores and social contextual information such as peer trends and life stage, financial institutions can offer suggestions for score improvement.
Computational social science in action
Using the ecosystem.Ai workbench, models can be easily deployed, tested and pushed into production. Selecting a model type such as Auto-ML, and linking it to your feature store, you can generate the default hyper-parameters automatically. From there, you can assign an algorithm such as XG boost to use and test. The model will score in real time in a production environment, and measure client feedback. It then continuously adapts to new parameters, updating features based on changes in real time. Another function in the workbench allows for a continuous pipeline of the data science process. Using the ecosystem.Ai workbench, you can experiment with various hyper-parameters, and perform tests entirely independent of the production environment. This means that you can test alternative models in parallel;, without disrupting the models you already have in production.
By prioritising what is good for your customers over short-term gains, financial services can cultivate profitable, long-term customer relationships and enhance CLV. CLV recognizes customers not as uniform entities, but dynamic beings with different characteristics, preferences and lifetime behavior. Traditionally these factors are used for targeted marketing and value propositions. But without considering financial wellness in tandem with this, CLV would quickly dry up.
The most problematic element in the world of wellness is a difficulty in quantifying lost opportunities. A company won’t know that financial wellness can significantly reduce credit risk, foster growth in assets and savings and fuel long-term engagement with customers, if their vision is clouded by short-term profitability. In ecosystem.Ai’s low-code environment, you can test and see the results for yourself.
